SoundMind: A Machine Learning and Web-Based Application for Depression Detection and Cure

This paper presents a machine learning and web-based application for the detection of depression. The system mainly serves two components: two machine-learning-based models to detect depression and a web-based application. The first machine learning model is implemented to classify the positive and negative text entered by the user/patient. The negative text states the use of words indicating depression, which can be termed as one factor in deciding a patient's mental health. The model is built using libraries such as Natural Language Toolkit (NLTK), and WordCloud. The second model predicts the presence of depression based on multiple health-related features such as the patient’s data related to various other disorders he/she is having, age, weight, BMI, blood-related features such as levels of calcium, CO2, phosphorus, iron, etc., and work-life related parameters. The prediction is carried out based on the classification result implemented using Logistic Regression. The model predicts the results with 91.85% test accuracy, 93% precision, 99% recall, and 96% f1 score. The above-mentioned models are deployed on the web application. The web application not only helps in predicting mental health but also suggests the proper treatment to cure the condition.